Given a heterogeneous graph, the proximity preserving heterogeneous graph models rely on the notion of similarity between nodes in the graph space to learn the node embedding. The similarity in the graph space can be defined through random-walks visits [1]} or first/second-order proximity between nodes [2]}. Metapath2vec [3]} is a popular random-walk-based model that learns the embeddings of nodes in the heterogeneous graph by performing random walks through meta-path schema. Metapath2vec inspired several meta-path schema-based heterogeneous graph models. For instance, HIN2Vec [4]} learns node embeddings by estimating the presence of meta-path between two nodes. Meta-path schema weighting techniques are also proposed [5]}, [6]} to learn efficient node embeddings. Other random-walk-based approaches, proposed models that do not require specifying meta-paths. For instance, JUST [7]} proposed a Jump and Stay random-walk strategy while Hao et al.[8]} proposed an attention mechanism for performing random-walk on heterogeneous graphs. The first/second-order proximity-based methods define proximity based on the edge-weights or the number of times the nodes are part of a subgraph or meta-graphs [9]}. PTE [10]} decomposes a heterogeneous graph into multiple bipartite graphs and learns node embeddings by preserving the first-order and second-order proximity between nodes in each bipartite graph. The PTE model is trained through the proposed joint training framework. PTE is considered as a shallow network embedding method as it is implicitly factorizing a form of Laplacian matrix [11]}. The interested readers can refer to the surveys of heterogeneous graph models [12]}, [9]}.
